Smoothing Algorithm

Smoothing algorithms are used to address challenges posed by non-smoothness in various machine learning and optimization problems, aiming to improve model robustness, efficiency, and interpretability. Current research focuses on developing and analyzing smoothing techniques within specific contexts, such as knowledge graph embedding, quantile regression, and adversarial robustness of deep learning models, often employing methods like randomized smoothing, double-smoothing, and surrogate neural networks. These advancements enhance the performance and reliability of machine learning models across diverse applications, from knowledge graph completion and traffic control to image segmentation and explainable AI.